def reset_optimizer(self,
lr=0.0002,
beta1=0.5,
scheduler_gamma=0.3,
scheduler_step_size=5,
scheduler_type='StepLR'):
self.optimizer_G = Adam(list(self.netG_A2B.parameters()) +
list(self.netG_B2A.parameters()),
lr=lr,
betas=(beta1, 0.999))
self.optimizer_D = Adam(list(self.netD_A.parameters()) +
list(self.netD_B.parameters()),
lr=lr,
betas=(beta1, 0.999))
self.optimizers = []
self.optimizers.append(self.optimizer_G)
self.optimizers.append(self.optimizer_D)
if scheduler_type == 'StepLR':
self.schedulers = [
lr_scheduler.StepLR(optimizer,
scheduler_step_size,
gamma=scheduler_gamma)
for optimizer in self.optimizers
]
elif scheduler_type == 'linear_decay':
self.schedulers = [
lr_scheduler.LambdaLR(optimizer,
utils.Linear_decay(scheduler_step_size))
for optimizer in self.optimizers
]
else:
raise Exception('unknown scheduler type')
def __init__(self, N, lr=0.1, theta=None):
self.N = N
self.optim = Adam(lr=lr)
self.sp = Softplus()
self.var = 0.01 * np.random.normal(size=2 * N + 1)
self.a = self.sp(self.var[0:N])
self.b = self.sp(self.var[N:2 * N])
self.theta = np.random.gamma(1., 1.) if theta is None else theta
self.var[-1] = self.sp.inv(self.theta)
def compile(self, train_batch_size):
print("Compiling functions...")
train_input = T.tensor4()
target_y = T.matrix()
target_x = T.matrix()
train_output, g_y, g_x = self.get_train_output(train_input,
train_batch_size)
classification_loss = self.get_NLL_cost(train_output[-1], self.target)
tracking_loss = self.get_tracking_cost(g_y, g_x, target_y, target_x)
loss = 5 * classification_loss + tracking_loss
updates = Adam(loss, self.params, lr=self.learning_rate)
# updates = self.get_updates(loss, self.params, self.learning_rate)
self.train_func = theano.function(
inputs=[train_input, self.target, target_y, target_x],
outputs=[train_output[-1], loss],
updates=updates,
allow_input_downcast=True)
h_tm1 = T.matrix()
c_tm1 = T.matrix()
predict_output, h, c, read, g_x, g_y, delta, sigma_sq = \
self.get_predict_output(self.input, h_tm1, c_tm1)
self.predict_func = theano.function(
inputs=[self.input, h_tm1, c_tm1],
outputs=[predict_output, h, c, read, g_x, g_y, delta, sigma_sq],
allow_input_downcast=True)
print("Done!")
def compile(self, loss='binary_cross_entropy', optimizer='adam'):
if loss == 'binary_cross_entropy':
self.loss_function = binary_cross_entropy
else:
raise ValueError(f"{loss} has not been added yet!")
if optimizer == 'adam':
self.optimizer = Adam()
self.optimizer.init_params(self.layers)
else:
raise ValueError(f"{loss} has not been added yet!")
for idx, layer in enumerate(self.layers):
print(
f"Layer {str(idx+1)} ; Name: {layer.__class__.__name__} ; Output Shape: {layer.output_shape}"
)
def __init__(self):
self.__load_dataset()
self.optimizer = Adam(cf.learning_rate, cf.beta_1, cf.beta_2,
cf.epsilon)
self.model = None
self.history = None
self.loss = None
self.accuracy = None
return
def __init__(self, dim_input, dim_hidden_layers, dim_output):
# dim_hidden_layers in a list with ith element being no. of nodes in hidden layer i
self.W = []
self.B = []
self.layers = []
self.X = T.dmatrix()
self.Y = T.dmatrix() # reward times action vector
for i in range(len(dim_hidden_layers) + 1):
w = None
lyr = None
if i == 0:
w = theano.shared(
np.array(np.random.rand(dim_input, dim_hidden_layers[0]),
dtype=theano.config.floatX))
b = theano.shared(
np.zeros((dim_hidden_layers[0], ),
dtype=theano.config.floatX))
lyr = self.layer(self.X, w, b)
elif i == len(dim_hidden_layers):
w = theano.shared(
np.array(np.random.rand(dim_hidden_layers[i - 1],
dim_output),
dtype=theano.config.floatX))
b = theano.shared(
np.zeros((dim_output, ), dtype=theano.config.floatX))
lyr = self.softmax_layer(self.layers[i - 1], w,
b) # output layer
else:
w = theano.shared(
np.array(np.random.rand(dim_hidden_layers[i - 1],
dim_hidden_layers[i]),
dtype=theano.config.floatX))
b = theano.shared(
np.zeros((dim_hidden_layers[i], ),
dtype=theano.config.floatX))
lyr = self.layer(self.layers[i - 1], w, b)
self.W.append(w)
self.B.append(b)
self.layers.append(lyr)
#cost equation
loss = T.sum(T.log(T.dot(self.layers[-1],
-self.Y.T))) #+ L1_reg*L1 + L2_reg*L2
#loss = self.layers[-1] - self.Y
#loss = T.sum(T.square(self.layers[-1]-self.Y))#+ L1_reg*L1 + L2_reg*L2
updates = Adam(loss, self.W + self.B) #+ Adam(loss, self.B)
#compile theano functions
self.backprop = theano.function(inputs=[self.X, self.Y],
outputs=loss,
updates=updates)
self.run_forward_batch = theano.function(inputs=[self.X],
outputs=self.layers[-1])
def name_model():
LSTM_SIZE = 300
layer1 = LSTM(len(CHARKEY), LSTM_SIZE, activation=T.tanh)
layer2 = Layer(LSTM_SIZE, len(CHARKEY), activation=lambda x:x)
params = layer1.params + [layer1.initial_hidden_state] + layer2.params
################# Train #################
train_data = T.ftensor3()
n_batch = train_data.shape[0]
train_input = T.concatenate([T.zeros([n_batch,1,len(CHARKEY)]),train_data[:,:-1,:]],1)
train_output = train_data
def _scan_train(last_out, last_state):
new_state = layer1.activate(last_out, last_state)
layer_out = layer1.postprocess_activation(new_state)
layer2_out = layer2.activate(layer_out)
new_out = T.nnet.softmax(layer2_out)
return new_out, new_state
outputs_info = [None, initial_state(layer1, n_batch)]
(scan_outputs, scan_states), _ = theano.scan(_scan_train, sequences=[train_input.dimshuffle([1,0,2])], outputs_info=outputs_info)
flat_scan_outputs = scan_outputs.dimshuffle([1,0,2]).reshape([-1,len(CHARKEY)])
flat_train_output = train_output.reshape([-1,len(CHARKEY)])
crossentropy = T.nnet.categorical_crossentropy(flat_scan_outputs, flat_train_output)
loss = T.sum(crossentropy)/T.cast(n_batch,'float32')
adam_updates = Adam(loss, params)
train_fn = theano.function([train_data],loss,updates=adam_updates)
################# Eval #################
length = T.iscalar()
srng = MRG_RandomStreams(np.random.randint(1, 1024))
def _scan_gen(last_out, last_state):
new_state = layer1.activate(last_out, last_state)
layer_out = layer1.postprocess_activation(new_state)
layer2_out = layer2.activate(layer_out)
new_out = T.nnet.softmax(T.shape_padleft(layer2_out))
sample = srng.multinomial(n=1,pvals=new_out)[0,:]
sample = T.cast(sample,'float32')
return sample, new_state
initial_input = np.zeros([len(CHARKEY)], np.float32)
outputs_info = [initial_input, layer1.initial_hidden_state]
(scan_outputs, scan_states), updates = theano.scan(_scan_gen, n_steps=length, outputs_info=outputs_info)
gen_fn = theano.function([length],scan_outputs,updates=updates)
return layer1, layer2, train_fn, gen_fn
def experiment(alg='ASNG',
eta_x=0.1,
eta_theta_factor=0.,
alpha=1.5,
K=5,
D=30,
maxite=100000,
log_file='log.csv',
seed=-1):
if seed >= 0:
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
nc = (K - 1) * D
f = fxc1(K, D, noise=True)
categories = K * np.ones(D, dtype=np.int)
if alg == 'ASNG':
opt_theta = AdaptiveSNG(categories,
alpha=alpha,
delta_init=nc**-eta_theta_factor)
elif alg == 'SNG':
opt_theta = SNG(categories, delta_init=nc**-eta_theta_factor)
elif alg == 'Adam':
opt_theta = Adam(categories,
alpha=nc**-eta_theta_factor,
beta1=0.9,
beta2=0.999)
else:
print('invalid algorithm!')
return
optimizer_x = torch.optim.SGD(f.parameters(),
lr=eta_x,
momentum=0.9,
weight_decay=0.,
nesterov=False)
lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer_x, maxite)
print('{}, eta_x={}, eta_theta_factor={} alpha={}'.format(
alg, eta_x, eta_theta_factor, alpha))
run(f,
opt_theta,
optimizer_x,
lr_scheduler=lr_scheduler,
maxite=maxite,
dispspan=100,
log_file=log_file)
def agent_init(self, agent_config):
"""Setup for the agent called when the experiment first starts.
Set parameters needed to setup the agent.
Assume agent_config dict contains:
{
network_pickle: string (optional),
network_config: dictionary,
optimizer_config: dictionary,
replay_buffer_size: integer,
minibatch_sz: integer,
num_replay_updates_per_step: float
discount_factor: float,
}
"""
self.replay_buffer = ReplayBuffer(agent_config['replay_buffer_size'],
agent_config['minibatch_sz'],
agent_config.get("seed"))
if "network_pickle" in agent_config:
self.network = pickle.load(
open(agent_config["network_pickle"], 'rb'))
else:
self.network = ActionValueNetwork(agent_config['network_config'])
self.optimizer = Adam(self.network.layer_sizes,
agent_config["optimizer_config"])
self.num_actions = agent_config['network_config']['num_actions']
self.num_replay = agent_config['num_replay_updates_per_step']
self.discount = agent_config['gamma']
self.tau = agent_config['tau']
self.rand_generator = np.random.RandomState(agent_config.get("seed"))
self.last_state = None
self.last_action = None
self.sum_rewards = 0
self.episode_steps = 0
def train(self, x, y, eta, batch=100, eps=1.e-6, max_iter=10000, seed=None,
x_test=None, y_test=None, early_stop=True, l2=None):
if self.r[0] != x.shape[1]:
self.r = [x.shape[1]] + self.r
self.npars = self.nparams()
print('{} parameters to fit'.format(self.npars[-1]))
self.l2 = l2
self.eta = eta
self.early_stop = early_stop and x_test is not None
self.N = x.shape[0]
self.eps = eps
self.err = False
self.log = {key: [] for key in ['j', 'tj']}
if seed:
np.random.seed(seed)
# allocate all weights
self.wght = inirand(self.npars[-1], 1, coef=2)
self.grad = np.empty(self.wght.shape)
# make weight vies for each layer
self.wl, self.gl = [self.weight_views(a) for a in [self.wght, self.grad]]
self.set_bias_mask()
def eval(check_condition=True):
self.log['j'].append(self.evaluate(x, y))
if x_test is not None:
self.log['tj'].append(self.evaluate(x_test, y_test))
if check_condition:
return self.stop_conditions()
eval(False)
# mini-batches
batches = self.make_batches(x, y, batch)
print('{} batches'.format(len(batches)))
# adaptive moments
self.adam = Adam()
cur_iter = 0
while cur_iter < max_iter:
if not cur_iter % 100:
print('it: {:5d}, J: {:.4f}'.format(cur_iter, self.log['j'][-1]))
for xi, yi in batches:
self.process(xi, yi)
if eval():
break
cur_iter += 1
if cur_iter == max_iter:
print('train: max number of iterations reached')
return not self.err
def reset_optimizer(self,
lr=0.0002,
beta1=0.5,
scheduler_gamma=0.3,
scheduler_step_size=5):
self.optimizer_G = Adam(list(self.netG_A2B.parameters()) +
list(self.netG_B2A.parameters()),
lr=lr,
betas=(beta1, 0.999))
self.optimizer_D = Adam(list(self.netD_A.parameters()) +
list(self.netD_B.parameters()),
lr=lr,
betas=(beta1, 0.999))
self.optimizers = []
self.optimizers.append(self.optimizer_G)
self.optimizers.append(self.optimizer_D)
self.schedulers = [
lr_scheduler.StepLR(optimizer,
scheduler_step_size,
gamma=scheduler_gamma)
for optimizer in self.optimizers
]
def get_optimizer(model,opt_name,lr,momentum=0.9,betas=(0.9,0.999)):
if opt_name=="vanilla_adam":
return torch.optim.Adam(model.parameters(),lr=lr,betas=betas),False
elif opt_name=="adam":
from adam import Adam
return Adam(model.parameters(),lr=lr,betas=betas),True
elif opt_name=="adamfast":
from .adam import AdamFast
return AdamFast(model.parameters(),lr=lr,betas=(0.9,0.999)),True
elif opt_name=="sgd_momentum":
return torch.optim.SGD(model.parameters(),lr=lr,momentum=momentum),False
elif opt_name=="sgd":
return torch.optim.SGD(model.parameters(),lr=lr),False
elif opt_name=="cgd":
from .cgd import CGD
return CGD(model.parameters(),lr=lr),True
def agent_init(self, agent_config):
self.replay_buffer = ReplayBuffer(agent_config['replay_buffer_size'],
agent_config['minibatch_sz'],
agent_config.get("seed"))
self.network = ActionValueNetwork(agent_config['network_config'])
self.optimizer = Adam(self.network.layer_sizes,
agent_config["optimizer_config"])
self.num_actions = agent_config['network_config']['num_actions']
self.num_replay = agent_config['num_replay_updates_per_step']
self.discount = agent_config['gamma']
self.tau = agent_config['tau']
self.rand_generator = np.random.RandomState(agent_config.get("seed"))
self.last_state = None
self.last_action = None
self.sum_rewards = 0
self.episode_steps = 0
def cnn_adam(X, Y, val_X, val_Y):
"""
CNN - ADAM
"""
model = Model(learning_rate=0.001,
batch_size=32,
epochs=200,
optimizer=Adam())
model.add(Conv2D(2, (3, 3), activation='tanh'))
model.add(Maxpool((2, 2), stride=2)) # 16x16
model.add(Dropout(0.5))
model.add(Conv2D(4, (3, 3), activation='tanh'))
model.add(Maxpool((2, 2), stride=2)) # 8x8
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(256, 32, activation='tanh'))
model.add(Dense(32, 1))
print("Begin Training")
model.train(X, Y, val_X, val_Y)
model.save_history("experiments/cnn-adam.csv")
def ann_adam_layers(X, Y, val_X, val_Y):
"""
Extra Layers with sigmoid and Adam
"""
model = Model(learning_rate=0.001,
batch_size=32,
epochs=200,
optimizer=Adam())
model.add(Dropout(0.5))
model.add(Dense(1024, 512, activation='sigmoid'))
model.add(Dropout(0.5))
model.add(Dense(512, 256, activation='sigmoid'))
model.add(Dropout(0.5))
model.add(Dense(256, 32, activation='sigmoid'))
model.add(Dropout(0.5))
model.add(Dense(32, 1))
print("Begin Training")
model.train(X, Y, val_X, val_Y)
model.save_history("experiments/ann-adam-1024-512-256-32-1.csv")
print(
"The CSV file is saved in the experiments folder. You can plot the graph using plot.py"
)
def main(args):
# Training settings
manual_seed = args.seed
# print("Random Seed: ", manual_seed)
# random.seed(manual_seed)
save_dir = args.save_dir
torch.manual_seed(manual_seed)
use_cuda = not args.no_cuda and torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
train_loader = torch.utils.data.DataLoader(datasets.MNIST(
'../data',
train=True,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])),
batch_size=args.batch_size,
shuffle=True,
**kwargs)
test_loader = torch.utils.data.DataLoader(datasets.MNIST(
'../data',
train=False,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])),
batch_size=args.test_batch_size,
shuffle=True,
**kwargs)
model_types = ["fc", "logistic"] if not args.drop_rate else ["fc"]
for model_type in model_types:
activations = ['relu', 'sigmoid'] if model_type == "fc" else ["None"]
for activation in activations:
for opt_type in ['adam', 'adagrad', 'rmsprop', 'sgd']:
if model_type == 'logistic':
model = LOGISTIC().to(device)
else:
model = FC(activation=activation,
drop_rate=args.drop_rate).to(device)
weight_decay = 1e-6 if model_type == "fc" else 0.
optimizers = {
'adam':
Adam(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay,
betas=args.betas),
'adagrad':
optim.Adagrad(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay),
'rmsprop':
optim.RMSprop(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay,
alpha=args.betas[1],
eps=1e-08,
momentum=0),
'sgd':
optim.SGD(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay,
momentum=args.momentum)
}
optimizer = optimizers[opt_type]
train_losses = []
test_losses = []
accuracies = []
test_times = []
for epoch in range(1, args.epochs + 1):
train_loss = train(args, model, device, train_loader,
optimizer, epoch)
t1 = time.time()
test_loss, accuracy = test(args, model, device,
test_loader)
test_time = time.time() - t1
train_losses.append(train_loss)
test_losses.append(test_loss)
accuracies.append(accuracy)
test_times.append(test_time)
PATH = os.path.join(
save_dir, "{}/{}_{}_{}.pt".format(
"drop" if args.drop_rate else "base", activation,
model_type, opt_type))
torch.save(
{
'args': args,
'opt_type': opt_type,
'train_losses': train_losses,
'test_losses': test_losses,
'accuracies': accuracies,
"times": test_times,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
}, PATH)
print("\nModel saved in \n" + PATH)
batch_size = 3
max_epoch = 1000
text = 'You said good-bye and I said hello.'
cbm = CountBasedMethod()
word_list = cbm.text_to_word_list(text)
word_to_id, id_to_word, corpus = cbm.preprocess(word_list)
vocab_size = len(word_to_id)
sw = SimpleWord2Vec()
contexts, target = sw.create_contexts_target(corpus)
contexts = sw.convert_to_one_hot(contexts, vocab_size)
target = sw.convert_to_one_hot(target, vocab_size)
model = SimpleCBOW(vocab_size, hidden_size)
optimiser = Adam()
trainer = Trainer(model, optimiser)
trainer.fit(contexts, target, max_epoch, batch_size)
file_path = '../img/simple_cbow.png'
trainer.save_plot_image(file_path)
word_vecs = model.word_vecs
for word_id, word in id_to_word.items():
print(word, word_vecs[word_id])
# you [ 0.7656109 -0.9978437 1.0643483 1.5883044 0.760913 ]
# said [-1.259723 0.21088624 -0.06925534 0.24457063 -1.2771223 ]
# good-bye [ 1.097698 -0.89157814 0.80477166 0.11352278 1.1290942 ]
# and [-1.0565615 1.3495133 -1.3335469 1.4657804 -1.0490868]
# i [ 1.082961 -0.8998216 0.787367 0.11362309 1.1094831 ]
running_mean_mid = None
running_var_mid = None
running_mean_out = None
running_var_out = None
break
elif load_yn == "y":
break
else:
print("illegal input from keyboard.")
eponum = 10
#update_variable
#lr = 0.01 #learn rate
w_one_adamclass = Adam(w_one.shape)
b_one_adamclass = Adam(b_one.shape)
w_two_adamclass = Adam(w_two.shape)
b_two_adamclass = Adam(b_two.shape)
gamma_mid_adamclass = Adam(gamma_mid.shape)
beta_mid_adamclass = Adam(beta_mid.shape)
gamma_out_adamclass = Adam(gamma_out.shape)
beta_out_adamclass = Adam(beta_out.shape)
w_conv_adamclass = Adam(w_conv.shape)
b_conv_adamclass = Adam(b_conv.shape)
for i in range(int(eponum * (trainsize / 100))): #i <- N/B * 100 or so
rannumlist = numpy.random.choice(trainsize, 100, replace=False)
image = (dlist[rannumlist] / 255) #.T
#xlist.shape=[784,100]
ylist = anslist[rannumlist]
def train(model, train_loader, test_loader, gen_loader, configs):
# model.train()
optimizer = Adam(configs.lr, amsgrad=True)
# # optimizer, it's better to set up lr for some modules separately so that the whole training become more stable
# optimizer = optim.Adamax([
# {'params': model.reader.parameters(), 'lr': 0.2 * configs.lr},
# {'params': model.h_mean.parameters(), 'lr': 0.1 * configs.lr},
# {'params': model.h_var.parameters(), 'lr': 0.1 * configs.lr},
# {'params': model.writer.parameters()},
# {'params': model.vis_dist.parameters()},
# {'params': model.pos_dist.parameters()},
# {'params': model.combine.parameters()},
# {'params': model.describe.parameters()},
# {'params': model.box_vae.parameters(), 'lr': 10 * configs.lr},
# {'params': model.offset_vae.parameters(), 'lr': 10 * configs.lr},
# {'params': model.renderer.parameters()},
# {'params': model.bias_mean.parameters()},
# {'params': model.bias_var.parameters()}
load_epoch = 40
# ], lr=configs.lr)
ifmask = True
# st()
x = tf.random.uniform([1, 64, 64, 3])
treex = pickle.load(
open(
"../PnpNet_tf_eager/data/CLEVR/CLEVR_64_MULTI_LARGE/trees/train/CLEVR_new_000002.tree",
"rb"))
trees = [treex]
# # st()
rec_loss, kld_loss, pos_loss, modelout = model(
x,
trees,
"filenames",
alpha=0.6,
ifmask=ifmask,
maskweight=configs.maskweight)
# load_epoch =10
# saver = tfe.Saver(model.all_trainable_variables)
# saver.restore(osp.join(configs.exp_dir, 'checkpoints_eager', 'model_epoch_{0}'.format(load_epoch)))
# print("Weights restored for {} from epoch {}".format(len(model.all_trainable_variables),load_epoch))
# model.cuda()
model.load_weights(
osp.join(configs.exp_dir, 'checkpoints_eager',
'model_epoch_{0}'.format(load_epoch)))
# st()
trainer = PNPNetTrainer(model=model,
optimizer=optimizer,
train_loader=train_loader,
val_loader=test_loader,
gen_loader=gen_loader,
configs=configs)
minloss = 1000
for epoch_num in range(load_epoch, configs.epochs + 1):
timestamp_start = datetime.datetime.now(
pytz.timezone('America/New_York'))
# trainer.train_epoch(epoch_num, timestamp_start)
if epoch_num % configs.save_interval == 0 and epoch_num > 0:
model.save_weights(
osp.join(configs.exp_dir, 'checkpoints_eager',
'model_epoch_{0}'.format(epoch_num)))
print("Model saved")
# if epoch_num % configs.validate_interval == 0 and epoch_num > 0:
# minloss = trainer.validate(epoch_num, timestamp_start, minloss)
if epoch_num % configs.sample_interval == 0 and epoch_num > 0:
trainer.sample(epoch_num,
sample_num=4,
timestamp_start=timestamp_start)
trainer.train_epoch(epoch_num, timestamp_start)
def pre_train(dataloader,
test_loader,
dict_loader,
dataloader_test,
mask_labels,
total_epochs=50,
learning_rate=1e-4,
use_gpu=True,
seed=123):
args = parser.parse_args()
pprint(args)
num_bits = args.num_bits
model = CNN(model_name='alexnet', bit=num_bits, class_num=args.num_class)
criterion = custom_loss(num_bits=num_bits)
arch = 'cnn_'
filename = arch + args.dataset + '_' + str(num_bits) + "bits"
checkpoint_filename = os.path.join(args.checkpoint, filename + '.pt')
if use_gpu:
model = model.cuda()
model = torch.nn.DataParallel(model,
device_ids=range(
torch.cuda.device_count()))
criterion = criterion.cuda()
torch.cuda.manual_seed(seed)
running_loss = 0.0
start_epoch = 0
batch_time = AverageMeter()
data_time = AverageMeter()
end = time.time()
best_prec = -99999
k = 10500
n_samples = 200000
alpha = 0.4
alpha_1 = 0.99
mask_labels = torch.from_numpy(mask_labels).long().cuda()
Z_h1 = torch.zeros(n_samples,
num_bits).float().cuda() # intermediate values
z_h1 = torch.zeros(n_samples, num_bits).float().cuda() # temporal outputs
h1 = torch.zeros(n_samples, num_bits).float().cuda() # current outputs
Z_h2 = torch.zeros(args.anchor_num,
num_bits).float().cuda() # intermediate values
z_h2 = torch.zeros(args.anchor_num,
num_bits).float().cuda() # temporal outputs
h2 = torch.zeros(args.anchor_num,
num_bits).float().cuda() # current outputs
for epoch in range(start_epoch, total_epochs):
model.train(True)
rampup_value = rampup(epoch)
rampdown_value = rampdown(epoch)
learning_rate = rampup_value * rampdown_value * 0.00005
adam_beta1 = rampdown_value * 0.9 + (1.0 - rampdown_value) * 0.5
adam_beta2 = step_rampup(epoch) * 0.99 + (1 -
step_rampup(epoch)) * 0.999
if epoch == 0:
u_w = 0.0
else:
u_w = rampup_value
u_w_m = u_w * 5
u_w_m = torch.autograd.Variable(torch.FloatTensor([u_w_m]).cuda(),
requires_grad=False)
optimizer = Adam(model.parameters(),
lr=learning_rate,
betas=(adam_beta1, adam_beta2),
eps=1e-8,
amsgrad=True)
anchors_data, anchor_Label = generate_anchor_vectors(dict_loader)
for iteration, data in enumerate(dataloader, 0):
anchor_index = np.arange(args.anchor_num)
np.random.shuffle(anchor_index)
anchor_index = anchor_index[:100]
anchor_index = torch.from_numpy(anchor_index).long().cuda()
anchor_inputs = anchors_data[anchor_index, :, :, :]
anchor_labels = anchor_Label[anchor_index, :]
inputs, labels, index = data['image'], data['labels'], data[
'index']
labels = labels.float()
mask_flag = Variable(mask_labels[index], requires_grad=False)
idx = (mask_flag > 0)
if index.shape[0] == args.batch_size:
anchor_batch_S, anchor_batch_W = CalcSim(
labels[idx, :].cuda(), anchor_labels.cuda())
if inputs.size(3) == 3:
inputs = inputs.permute(0, 3, 1, 2)
inputs = inputs.type(torch.FloatTensor)
zcomp_h1 = z_h1[index.cuda(), :]
zcomp_h2 = z_h2[anchor_index, :]
labeled_batch_S, labeled_batch_W = CalcSim(
labels[idx, :].cuda(), labels[idx, :].cuda())
if use_gpu:
inputs = Variable(inputs.cuda(), requires_grad=False)
anchor_batch_S = Variable(anchor_batch_S.cuda(),
requires_grad=False)
anchor_batch_W = Variable(anchor_batch_W.cuda(),
requires_grad=False)
labeled_batch_S = Variable(labeled_batch_S.cuda(),
requires_grad=False)
labeled_batch_W = Variable(labeled_batch_W.cuda(),
requires_grad=False)
# zero the parameter gradients
optimizer.zero_grad()
y_h1 = model(inputs)
y_h2 = model(anchor_inputs)
y = F.sigmoid(48 / num_bits * 0.4 *
torch.matmul(y_h1, y_h2.permute(1, 0)))
loss, l_batch_loss, m_loss = criterion(
y, y_h1, y_h2, anchor_batch_S, anchor_batch_W,
labeled_batch_S, labeled_batch_W, zcomp_h1, zcomp_h2,
mask_flag, u_w_m, epoch, num_bits)
h1[index, :] = y_h1.data.clone()
h2[anchor_index, :] = y_h2.data.clone()
# backward+optimize
loss.backward()
optimizer.step()
running_loss += loss.item()
Z_h2 = alpha_1 * Z_h2 + (1. - alpha_1) * h2
z_h2 = Z_h2 * (1. / (1. - alpha_1**(epoch + 1)))
print(
"Epoch[{}]({}/{}): Time:(data {:.3f}/ batch {:.3f}) Loss_H: {:.4f}/{:.4f}/{:.4f}"
.format(epoch, iteration, len(dataloader), data_time.val,
batch_time.val, loss.item(), l_batch_loss.item(),
m_loss.item()))
Z_h1 = alpha * Z_h1 + (1. - alpha) * h1
z_h1 = Z_h1 * (1. / (1. - alpha**(epoch + 1)))
if epoch % 1 == 0:
MAP = helpers.validate(model, dataloader_test, test_loader)
print("Test image map is:{}".format(MAP))
is_best = MAP > best_prec
best_prec = max(best_prec, MAP)
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
},
is_best,
prefix=arch,
num_bits=num_bits,
filename=checkpoint_filename)
return model
network.add(layers.Dense(10, activation='softmax'))
results_acc = []
result_acc = []
results_loss = []
result_loss = []
test_acc_results = []
test_loss_results = []
nonzero_weights = []
l = [
GRDA(lr=.005, c=.02),
SGD(lr=.005, nesterov=False),
SGD(lr=.005, nesterov=True),
Adagrad(lr=.005),
Adam(lr=.005, amsgrad=False),
Adam(lr=.005, amsgrad=True)
]
allcounts = np.sum([x.size for x in network.get_weights()])
for opt in l:
network.compile(optimizer=opt,
loss='categorical_crossentropy',
metrics=['accuracy'])
#network.save_weights('initial_weights.h5')
#network.load_weights('initial_weights.h5')
#initial_weights = network.get_weights()
result_acc = []
result_loss = []
test_loss = []
test_acc = []
'filter_num': 30,
'filter_size': 5,
'pad': 0,
'stride': 1,
}
hidden_size = 256
output_size = 10
network = Net(
input_dim=(1, 28, 28),
conv_param=conv_param,
hidden_size=hidden_size,
output_size=output_size,
weight_init_std=0.01,
)
optimizer = Adam()
train_size = x_train.shape[0]
batch_size = 64
learning_rate = 0.1
# epochs = 100
train_loss_list = []
train_acc_list = []
test_acc_list = []
epochs = 20
iter_per_epoch = max(train_size / batch_size, 1)
max_iter = int(epochs * iter_per_epoch)
def trajopt(wps,
writer,
init_vel=[0, 0.01],
n_pts=40,
constraint_weights=[0.01, 1, 0.0001, 0.001, 0.1, 0.1],
max_n_opts=1000,
lr=1e-6):
# define params
init_vel = from_numpy(np.array(init_vel))
constraint_weights = from_numpy(np.array(constraint_weights))
wps_init = tensor(wps, requires_grad=True)
wp_speeds_init = tensor(np.ones(len(wps) - 2) * 30, requires_grad=True)
seg_times_init = tensor(np.ones(len(wps) - 1) * 30, requires_grad=True)
# define bounds
wps_delta = from_numpy(
np.array([
CS['waypoint_tol'], CS['waypoint_tol'],
np.deg2rad(CS['angle_tol'])
]))
wps_lo, wps_hi = wps_init - wps_delta, wps_init + wps_delta
seg_times_lo = from_numpy(
np.linalg.norm(wps[1:, :2] - wps[:-1, :2], axis=1) / CS['max_vel'])
trajs_lo, trajs_hi = from_numpy(CS['xyp_lims_lo']), from_numpy(
CS['xyp_lims_hi'])
# define optimizers
opts = {
'wps':
Adam(wps_init.flatten(),
alpha=lr,
lo=wps_lo.flatten(),
hi=wps_hi.flatten()),
'seg_times':
Adam(seg_times_init, alpha=lr, lo=seg_times_lo),
'wp_speeds':
Adam(wp_speeds_init, alpha=lr)
}
costs = []
n_opts = trange(max_n_opts)
for n_opt in n_opts:
wps = opts['wps'].params.view(wps_init.shape)
seg_times = opts['seg_times'].params
wp_speeds = opts['wp_speeds'].params
# compute trajs
trajs, d_trajs = gen_trajs(wps, wp_speeds, init_vel, n_pts)
if n_opt == 0:
trajs_init, d_trajs_init = get_numpy(trajs), get_numpy(d_trajs)
total_time_init = get_numpy(torch.sum(seg_times))
# compute needed values
velocities = d_trajs / torch.unsqueeze(torch.unsqueeze(seg_times, 1),
1)
speeds = torch.norm(velocities, dim=2)
accelerations = (speeds[:, 1:] - speeds[:, :-1]) / torch.unsqueeze(
seg_times, 1) * n_pts
angles = torch.atan2(d_trajs[:, :, 1], d_trajs[:, :, 0])
betas = torch.asin(torch.tanh(CS['wheelbase'] / 2 * angles / speeds))
# betas = torch.atan2(d_trajs[:,:,1], d_trajs[:,:,0])
steering_angles = torch.atan(2 * torch.tan(betas))
# angles = torch.sin(betas) * 2 / CS['wheelbase'] * speeds
velocities_pred = torch.unsqueeze(speeds, 2) * torch.cat([
torch.unsqueeze(torch.cos(angles + betas), 2),
torch.unsqueeze(torch.sin(angles + betas), 2)
], 2)
# compute loss
total_time = torch.sum(seg_times)
constraint_costs = torch.stack([
# dynamics
torch.norm(velocities_pred - velocities),
# steering angle
torch.sum(
torch.pow(
torch.relu(steering_angles - CS['max_steering_angle']),
2)),
# acceleration
torch.sum(torch.pow(torch.relu(accelerations - CS['max_acc']), 2)),
# speed
torch.sum(torch.relu(torch.pow(speeds, 2) - CS['max_vel']**2)),
# traj bounds
torch.sum(
torch.pow(
torch.relu(trajs - trajs_hi) +
torch.relu(-trajs + trajs_lo), 2))
])
constraint_cost = constraint_costs @ constraint_weights
loss = total_time + constraint_cost
costs.append(
np.concatenate([[get_numpy(total_time)],
get_numpy(constraint_costs)]))
# Compute grad
grad_wps, grad_seg_times, grad_wp_speeds = grad(
loss, [wps, seg_times, wp_speeds])
grad_wps[torch.isnan(grad_wps)] = 0
grad_seg_times[torch.isnan(grad_seg_times)] = 0
grad_wp_speeds[torch.isnan(grad_wp_speeds)] = 0
# Step optimizer
opts['wps'].collect_grad(grad_wps.flatten())
opts['seg_times'].collect_grad(grad_seg_times)
opts['wp_speeds'].collect_grad(grad_wp_speeds)
opts['wps'].step()
opts['seg_times'].step()
opts['wp_speeds'].step()
# log progress
writer.add_scalar('/costs/loss', loss, n_opt)
writer.add_scalar('/costs/total_time', total_time, n_opt)
writer.add_scalar('/costs/total_constraints', constraint_cost, n_opt)
writer.add_scalar('/costs/dynamics', constraint_costs[0], n_opt)
writer.add_scalar('/costs/steering_angle', constraint_costs[1], n_opt)
writer.add_scalar('/costs/acceleration', constraint_costs[2], n_opt)
writer.add_scalar('/costs/speed', constraint_costs[3], n_opt)
writer.add_scalar('/costs/traj_bounds', constraint_costs[4], n_opt)
writer.add_scalar('/grads/wps', grad_wps.mean(), n_opt)
writer.add_scalar('/grads/seg_times', grad_seg_times.mean(), n_opt)
writer.add_scalar('/grads/wp_speeds', grad_wp_speeds.mean(), n_opt)
for i in range(len(seg_times)):
writer.add_scalar('/seg_times/{}'.format(i), seg_times[i], n_opt)
for i in range(len(wp_speeds)):
writer.add_scalar('/wp_speeds/{}'.format(i), wp_speeds[i], n_opt)
n_opts.set_description(
'Loss {:.3f} | Time {:.3f} | TC {:.3f} | Dynamics {:.3f}'.format(
get_numpy(loss), get_numpy(total_time),
get_numpy(constraint_cost), get_numpy(constraint_costs[0])))
n_opts.refresh()
return {
'wps': get_numpy(opts['wps'].params.view(wps_init.shape)),
'wp_speeds': get_numpy(opts['wp_speeds'].params),
'seg_times': get_numpy(opts['seg_times'].params),
'trajs_init': trajs_init,
'd_trajs_init': d_trajs_init,
'trajs': get_numpy(trajs),
'd_trajs': get_numpy(d_trajs),
'velocities': get_numpy(velocities),
'speeds': get_numpy(speeds),
'accelerations': get_numpy(accelerations),
'steering_angles': get_numpy(steering_angles),
'velocities_pred': get_numpy(velocities_pred),
'betas': get_numpy(betas),
'total_time_init': total_time_init,
'total_time': get_numpy(total_time),
'loss': get_numpy(loss),
'costs': np.array(costs),
'constraint_costs': get_numpy(constraint_costs),
'constraint_cost': get_numpy(constraint_cost)
}
class GammaGamma(object):
def __init__(self, N, lr=0.1, theta=None):
self.N = N
self.optim = Adam(lr=lr)
self.sp = Softplus()
self.var = 0.01 * np.random.normal(size=2 * N + 1)
self.a = self.sp(self.var[0:N])
self.b = self.sp(self.var[N:2 * N])
self.theta = np.random.gamma(1., 1.) if theta is None else theta
self.var[-1] = self.sp.inv(self.theta)
#reparametrization trick
def reparam(self, debug=False):
a = self.a
b = self.b
z = 0.2*np.ones(self.N) if debug else \
1e-15 + np.random.rand(self.N)*(1.-1e-15) #by default, std normal
w = np.zeros(self.N)
dwda = np.zeros(self.N)
#approximation for shape param gradient
small = a < 1000
#idea is understandable, math still hazy
if np.any(small):
a_ = a[small]
b_ = b[small]
z_ = z[small]
# reparam y ~ gamma(a+1, b)
y_ = gamma.ppf(z_, a_ + 1,
scale=b_**-1) #gamma param -- rate is 1/scale
dyda_ = (gamma.ppf(z_, a_ + 1 + 1e-5, scale=b_**-1) - y_) / 1e-5
u_ = 0.3*np.ones(a_.shape) if debug else \
1e-15 + np.random.rand(np.prod(a_.shape)).reshape(a_.shape)*(1.-1e-15)
ua_ = u_**(1. / a_)
w[small] = ua_ * y_
dwda[small] = -log(u_) * w[small] / (a_**2) + ua_ * dyda_
large = np.logical_not(small)
if np.any(large):
#okay
a_ = a[large]
b_ = b[large]
sqa_ = np.sqrt(a_)
z_ = 0.3 * np.ones(a_.shape) if debug else np.random.normal(
size=a_.shape)
w[large] = (a_ + sqa_ * z_) / b_
dwda[large] = (1. + 0.5 * z_ / sqa_) / b_
dwdb = -w / b
w[w < 1e-40] = 1e-40
self.w = w
self.dwda = dwda
self.dwdb = dwdb
return w
def sample_p(self):
return np.random.gamma(self.theta, 1., self.N)
def log_p(self, w):
theta = self.theta
lp = (theta - 1) * log(w) - w - gammaln(theta)
return lp.sum(lp.ndim - 1).mean()
def log_q(self, w):
a = self.a
b = self.b
lq = a * log(b) + (a - 1) * log(w) - b * w - gammaln(a)
return lq.sum(lq.ndim - 1).mean()
def sample_q(self, S=1):
return np.random.gamma(self.a, scale=self.b**-1,
size=(S, self.N)) #scale = 1/rate
#details yet to figure out
def step(self, dlldw):
w = self.w
a = self.a
b = self.b
dwda = self.dwda
dwdb = self.dwdb
N = self.N
theta = self.theta
dlpdw = (theta - 1) / (w + eps) - 1.
dljdw = dlldw + dlpdw
dlqda = log(b) + (a - 1) * dwda / w + log(w) - b * dwda - digamma(a)
dlqdb = a / b + (a - 1) * dwdb / w - w - b * dwdb
dLda = dljdw * dwda - dlqda
dLdb = dljdw * dwdb - dlqdb
dLdtheta = -N * digamma(theta) + log(w).sum()
grad = np.append(
np.concatenate(
[dLda * self.sp.jacobian(a), dLdb * self.sp.jacobian(b)]),
dLdtheta * self.sp.jacobian(theta))
self.var = self.optim.step(self.var, -grad)
self.a = self.sp(self.var[0:N])
self.b = self.sp(self.var[N:2 * N])
self.theta = self.sp(self.var[-1])
def get_hp_name(self):
return ['theta']
def get_hp(self):
return [self.theta]
def print_hp(self):
return 'theta %.4f' % self.theta
def compress(args):
"""Compresses an image, or a batch of images of the same shape in npy format."""
from configs import get_eval_batch_size
if args.input_file.endswith('.npy'):
# .npy file should contain N images of the same shapes, in the form of an array of shape [N, H, W, 3]
X = np.load(args.input_file)
else:
# Load input image and add batch dimension.
from PIL import Image
x = np.asarray(Image.open(args.input_file).convert('RGB'))
X = x[None, ...]
num_images = int(X.shape[0])
img_num_pixels = int(np.prod(X.shape[1:-1]))
X = X.astype('float32')
X /= 255.
eval_batch_size = get_eval_batch_size(img_num_pixels)
dataset = tf.data.Dataset.from_tensor_slices(X)
dataset = dataset.batch(batch_size=eval_batch_size)
# https://www.tensorflow.org/api_docs/python/tf/compat/v1/data/Iterator
# Importantly, each sess.run(op) call will consume a new batch, where op is any operation that depends on
# x. Therefore if multiple ops need to be evaluated on the same batch of data, they have to be grouped like
# sess.run([op1, op2, ...]).
# x = dataset.make_one_shot_iterator().get_next()
x_next = dataset.make_one_shot_iterator().get_next()
x_ph = x = tf.placeholder(
'float32',
(None, *X.shape[1:])) # keep a reference around for feed_dict
#### BEGIN build compression graph ####
# Instantiate model.
analysis_transform = AnalysisTransform(args.num_filters)
synthesis_transform = SynthesisTransform(args.num_filters)
hyper_analysis_transform = HyperAnalysisTransform(args.num_filters)
hyper_synthesis_transform = HyperSynthesisTransform(args.num_filters,
num_output_filters=2 *
args.num_filters)
entropy_bottleneck = tfc.EntropyBottleneck()
# Initial values for optimization
y_init = analysis_transform(x)
z_init = hyper_analysis_transform(y_init)
y = tf.placeholder('float32', y_init.shape)
y_tilde = y + tf.random.uniform(tf.shape(y), -0.5, 0.5)
z = tf.placeholder('float32', z_init.shape)
# sample z_tilde from q(z_tilde|x) = q(z_tilde|h_a(g_a(x))), and compute the pdf of z_tilde under the flexible prior
# p(z_tilde) ("z_likelihoods")
z_tilde, z_likelihoods = entropy_bottleneck(z, training=True)
z_hat = entropy_bottleneck._quantize(
z, 'dequantize') # rounded (with median centering)
mu, sigma = tf.split(hyper_synthesis_transform(z_tilde),
num_or_size_splits=2,
axis=-1)
sigma = tf.exp(sigma) # make positive
# need to handle images with non-standard sizes during compression; mu/sigma must have the same shape as y
y_shape = tf.shape(y_tilde)
mu = mu[:, :y_shape[1], :y_shape[2], :]
sigma = sigma[:, :y_shape[1], :y_shape[2], :]
scale_table = np.exp(
np.linspace(np.log(SCALES_MIN), np.log(SCALES_MAX), SCALES_LEVELS))
conditional_bottleneck = tfc.GaussianConditional(sigma,
scale_table,
mean=mu)
# compute the pdf of y_tilde under the conditional prior/entropy model p(y_tilde|z_tilde)
# = N(y_tilde|mu, sigma^2) conv U(-0.5, 0.5)
y_likelihoods = conditional_bottleneck._likelihood(
y_tilde) # p(\tilde y | \tilde z)
if conditional_bottleneck.likelihood_bound > 0:
likelihood_bound = conditional_bottleneck.likelihood_bound
y_likelihoods = math_ops.lower_bound(y_likelihoods, likelihood_bound)
y_hat = conditional_bottleneck._quantize(
y, 'dequantize') # rounded (with mean centering)
x_tilde = synthesis_transform(y_tilde)
x_shape = tf.shape(x)
x_tilde = x_tilde[:, :x_shape[1], :x_shape[
2], :] # crop reconstruction to have the same shape as input
# Total number of bits divided by number of pixels.
# - log p(\tilde y | \tilde z) - log p(\tilde z) - - log q(\tilde z | \tilde y)
axes_except_batch = list(range(1, len(x.shape))) # should be [1,2,3]
y_bpp = tf.reduce_sum(-tf.log(y_likelihoods), axis=axes_except_batch) / (
np.log(2) * img_num_pixels)
z_bpp = tf.reduce_sum(-tf.log(z_likelihoods), axis=axes_except_batch) / (
np.log(2) * img_num_pixels)
eval_bpp = y_bpp + z_bpp # shape (N,)
train_bpp = tf.reduce_mean(eval_bpp)
# Mean squared error across pixels.
train_mse = tf.reduce_mean(tf.squared_difference(x, x_tilde))
# Multiply by 255^2 to correct for rescaling.
# float_train_mse = train_mse
# psnr = - 10 * (tf.log(float_train_mse) / np.log(10)) # float MSE computed on float images
train_mse *= 255**2
# The rate-distortion cost.
if args.lmbda < 0:
args.lmbda = float(args.runname.split('lmbda=')[1].split('-')
[0]) # re-use the lmbda as used for training
print(
'Defaulting lmbda (mse coefficient) to %g as used in model training.'
% args.lmbda)
if args.lmbda > 0:
rd_loss = args.lmbda * train_mse + train_bpp
else:
rd_loss = train_bpp
rd_gradients = tf.gradients(rd_loss, [y, z])
# Bring both images back to 0..255 range, for evaluation only.
x *= 255
x_tilde = tf.clip_by_value(x_tilde, 0, 1)
x_tilde = tf.round(x_tilde * 255)
mse = tf.reduce_mean(tf.squared_difference(x, x_tilde),
axis=axes_except_batch) # shape (N,)
psnr = tf.image.psnr(x_tilde, x, 255) # shape (N,)
msssim = tf.image.ssim_multiscale(x_tilde, x, 255) # shape (N,)
msssim_db = -10 * tf.log(1 - msssim) / np.log(10) # shape (N,)
with tf.Session() as sess:
# Load the latest model checkpoint, get compression stats
save_dir = os.path.join(args.checkpoint_dir, args.runname)
latest = tf.train.latest_checkpoint(checkpoint_dir=save_dir)
tf.train.Saver().restore(sess, save_path=latest)
eval_fields = [
'mse', 'psnr', 'msssim', 'msssim_db', 'est_bpp', 'est_y_bpp',
'est_z_bpp'
]
eval_tensors = [mse, psnr, msssim, msssim_db, eval_bpp, y_bpp, z_bpp]
all_results_arrs = {key: []
for key in eval_fields
} # append across all batches
log_itv = 100
if save_opt_record:
log_itv = 10
rd_lr = 0.005
rd_opt_its = 2000
from adam import Adam
batch_idx = 0
while True:
try:
x_val = sess.run(x_next)
x_feed_dict = {x_ph: x_val}
# 1. Perform R-D optimization conditioned on ground truth x
print('----RD Optimization----')
y_cur, z_cur = sess.run([y_init, z_init],
feed_dict=x_feed_dict) # np arrays
adam_optimizer = Adam(lr=rd_lr)
opt_record = {
'its': [],
'rd_loss': [],
'rd_loss_after_rounding': []
}
for it in range(rd_opt_its):
grads, obj, mse_, train_bpp_, psnr_ = sess.run(
[rd_gradients, rd_loss, train_mse, train_bpp, psnr],
feed_dict={
y: y_cur,
z: z_cur,
**x_feed_dict
})
y_cur, z_cur = adam_optimizer.update([y_cur, z_cur], grads)
if it % log_itv == 0 or it + 1 == rd_opt_its:
psnr_ = psnr_.mean()
if args.verbose:
y_hat_, z_hat_ = sess.run([y_hat, z_hat],
feed_dict={
y: y_cur,
z: z_cur
})
bpp_after_rounding, psnr_after_rounding, rd_loss_after_rounding = sess.run(
[train_bpp, psnr, rd_loss],
feed_dict={
y_tilde: y_hat_,
z_tilde: z_hat_,
**x_feed_dict
})
psnr_after_rounding = psnr_after_rounding.mean()
print(
'it=%d, rd_loss=%.4f mse=%.3f bpp=%.4f psnr=%.4f\t after rounding: rd_loss=%.4f, bpp=%.4f psnr=%.4f'
% (it, obj, mse_, train_bpp_, psnr_,
rd_loss_after_rounding, bpp_after_rounding,
psnr_after_rounding))
opt_record['rd_loss_after_rounding'].append(
rd_loss_after_rounding)
else:
print(
'it=%d, rd_loss=%.4f mse=%.3f bpp=%.4f psnr=%.4f'
% (it, obj, mse_, train_bpp_, psnr_))
opt_record['its'].append(it)
opt_record['rd_loss'].append(obj)
print()
# this is the latents we end up transmitting
y_hat_, z_hat_ = sess.run([y_hat, z_hat],
feed_dict={
y: y_cur,
z: z_cur
})
# If requested, transform the quantized image back and measure performance.
eval_arrs = sess.run(eval_tensors,
feed_dict={
y_tilde: y_hat_,
z_tilde: z_hat_,
**x_feed_dict
})
for field, arr in zip(eval_fields, eval_arrs):
all_results_arrs[field] += arr.tolist()
batch_idx += 1
except tf.errors.OutOfRangeError:
break
for field in eval_fields:
all_results_arrs[field] = np.asarray(all_results_arrs[field])
input_file = os.path.basename(args.input_file)
results_dict = all_results_arrs
trained_script_name = args.runname.split('-')[0]
script_name = os.path.splitext(os.path.basename(__file__))[
0] # current script name, without extension
# save RD evaluation results
prefix = 'rd'
save_file = '%s-%s-input=%s.npz' % (prefix, args.runname, input_file)
if script_name != trained_script_name:
save_file = '%s-%s-lmbda=%g+%s-input=%s.npz' % (
prefix, script_name, args.lmbda, args.runname, input_file)
np.savez(os.path.join(args.results_dir, save_file), **results_dict)
if save_opt_record:
# save optimization record
prefix = 'opt'
save_file = '%s-%s-input=%s.npz' % (prefix, args.runname,
input_file)
if script_name != trained_script_name:
save_file = '%s-%s-lmbda=%g+%s-input=%s.npz' % (
prefix, script_name, args.lmbda, args.runname, input_file)
np.savez(os.path.join(args.results_dir, save_file), **opt_record)
for field in eval_fields:
arr = all_results_arrs[field]
print('Avg {}: {:0.4f}'.format(field, arr.mean()))
class Agent:
def __init__(self):
self.name = "expected_sarsa_agent"
def agent_init(self, agent_config):
"""Setup for the agent called when the experiment first starts.
Set parameters needed to setup the agent.
Assume agent_config dict contains:
{
network_pickle: string (optional),
network_config: dictionary,
optimizer_config: dictionary,
replay_buffer_size: integer,
minibatch_sz: integer,
num_replay_updates_per_step: float
discount_factor: float,
}
"""
self.replay_buffer = ReplayBuffer(agent_config['replay_buffer_size'],
agent_config['minibatch_sz'],
agent_config.get("seed"))
if "network_pickle" in agent_config:
self.network = pickle.load(
open(agent_config["network_pickle"], 'rb'))
else:
self.network = ActionValueNetwork(agent_config['network_config'])
self.optimizer = Adam(self.network.layer_sizes,
agent_config["optimizer_config"])
self.num_actions = agent_config['network_config']['num_actions']
self.num_replay = agent_config['num_replay_updates_per_step']
self.discount = agent_config['gamma']
self.tau = agent_config['tau']
self.rand_generator = np.random.RandomState(agent_config.get("seed"))
self.last_state = None
self.last_action = None
self.sum_rewards = 0
self.episode_steps = 0
def policy(self, state):
"""
Args:
state (Numpy array): the state.
Returns:
the action.
"""
action_values = self.network.get_action_values(state)
probs_batch = self.softmax(action_values, self.tau)
action = self.rand_generator.choice(self.num_actions,
p=probs_batch.squeeze())
return action
def agent_start(self, state):
"""The first method called when the experiment starts, called after
the environment starts.
Args:
state (Numpy array): the state from the
environment's evn_start function.
Returns:
The first action the agent takes.
"""
self.sum_rewards = 0
self.episode_steps = 0
self.last_state = np.array([state])
self.last_action = self.policy(self.last_state)
return self.last_action
def agent_step(self, reward, state):
"""A step taken by the agent.
Args:
reward (float): the reward received for taking the last action taken
state (Numpy array): the state from the
environment's step based, where the agent ended up after the
last step
Returns:
The action the agent is taking.
"""
self.sum_rewards += reward
self.episode_steps += 1
state = np.array([state])
action = self.policy(state)
self.replay_buffer.append(self.last_state, self.last_action, reward, 0,
state)
if self.replay_buffer.size() > self.replay_buffer.minibatch_size:
current_q = deepcopy(self.network)
for _ in range(self.num_replay):
experiences = self.replay_buffer.sample()
self.optimize_network(experiences, current_q)
self.last_state = state
self.last_action = action
return action
def agent_end(self, reward):
"""Run when the agent terminates.
Args:
reward (float): the reward the agent received for entering the
terminal state.
"""
self.sum_rewards += reward
self.episode_steps += 1
state = np.zeros_like(self.last_state)
self.replay_buffer.append(self.last_state, self.last_action, reward, 1,
state)
if self.replay_buffer.size() > self.replay_buffer.minibatch_size:
current_q = deepcopy(self.network)
for _ in range(self.num_replay):
experiences = self.replay_buffer.sample()
self.optimize_network(experiences, current_q)
def agent_message(self, message):
if message == "get_sum_reward":
return self.sum_rewards
else:
raise Exception("Unrecognized Message!")
def softmax(self, action_values, tau=1.0):
"""
Args:
action_values (Numpy array): A 2D array of shape (batch_size, num_actions).
The action-values computed by an action-value network.
tau (float): The temperature parameter scalar.
Returns:
A 2D array of shape (batch_size, num_actions). Where each column is a probability distribution over
the actions representing the policy.
"""
preferences = action_values / tau
max_preference = np.amax(preferences, 1)
reshaped_max_preference = max_preference.reshape((-1, 1))
exp_preferences = np.exp(preferences - reshaped_max_preference)
sum_of_exp_preferences = np.sum(exp_preferences, 1)
reshaped_sum_of_exp_preferences = sum_of_exp_preferences.reshape(
(-1, 1))
action_probs = exp_preferences / reshaped_sum_of_exp_preferences
action_probs = action_probs.squeeze()
return action_probs
def get_td_error(self, states, next_states, actions, rewards, terminals,
current_q):
"""
Args:
states (Numpy array): The batch of states with the shape (batch_size, state_dim).
next_states (Numpy array): The batch of next states with the shape (batch_size, state_dim).
actions (Numpy array): The batch of actions with the shape (batch_size,).
rewards (Numpy array): The batch of rewards with the shape (batch_size,).
discount (float): The discount factor.
terminals (Numpy array): The batch of terminals with the shape (batch_size,).
network (ActionValueNetwork): The latest state of the network that is getting replay updates.
current_q (ActionValueNetwork): The fixed network used for computing the targets,
and particularly, the action-values at the next-states.
Returns:
The TD errors (Numpy array) for actions taken, of shape (batch_size,)
"""
q_next_mat = np.apply_along_axis(current_q.get_action_values, 1,
next_states).squeeze()
probs_mat = self.softmax(q_next_mat, self.tau)
v_next_vec = np.einsum("ij,ij->i", probs_mat, q_next_mat)
v_next_vec *= (1 - terminals)
target_vec = rewards + self.discount * v_next_vec
q_mat = np.apply_along_axis(self.network.get_action_values, 1,
states).squeeze()
batch_indices = np.arange(q_mat.shape[0])
q_vec = np.array([q_mat[i][actions[i]] for i in batch_indices])
delta_vec = target_vec - q_vec
return delta_vec
def optimize_network(self, experiences, current_q):
"""
Args:
experiences (Numpy array): The batch of experiences including the states, actions,
rewards, terminals, and next_states.
discount (float): The discount factor.
network (ActionValueNetwork): The latest state of the network that is getting replay updates.
current_q (ActionValueNetwork): The fixed network used for computing the targets,
and particularly, the action-values at the next-states.
"""
states, actions, rewards, terminals, next_states = map(
list, zip(*experiences))
states = np.concatenate(states)
next_states = np.concatenate(next_states)
rewards = np.array(rewards)
terminals = np.array(terminals)
batch_size = states.shape[0]
delta_vec = self.get_td_error(states, next_states, actions, rewards,
terminals, current_q)
batch_indices = np.arange(batch_size)
delta_mat = np.zeros((batch_size, self.network.num_actions))
delta_mat[batch_indices, actions] = delta_vec
td_update = self.network.get_TD_update(states, delta_mat)
weights = self.optimizer.update_weights(self.network.get_weights(),
td_update)
self.network.set_weights(weights)
def main():
setupStartTime = time.time()
#load data
trainImages, trainLabels, testImages, testLabels = loadMNIST()
trainImages = binarize(trainImages)
testImages = binarize(testImages)
trainImagesByLabel, testImagesByLabel = \
sortMNISTByLabel(trainImages, trainLabels, testImages, testLabels)
#parameters
numberVisibleUnits = trainImages.shape[-1]
numberHiddenUnits = 200
#numberHiddenUnits = int(numberVisibleUnits * 2./3.)
temperature, nCDSteps = 1., 1
#sigma = 0.01
sigma = 2. / np.sqrt(numberVisibleUnits + numberHiddenUnits)
#gradientWalker = 'sgd'
gradientWalker = 'adam'
iterations, miniBatchSize = int(6e5), 10
internalRngSeed, externalRngSeed = 1337, 1234
rng = RandomState(seed=externalRngSeed)
plotNumber, plotStride = 5, 1
trainingReconstructionErrorOutputStride = 10
trainingOutputStride = iterations // 5
equilibrateFantasyForOutput = 100
#l1Coefficient = 1e-5
l1Coefficient = None
l2Coefficient = 1e-4
gamma = 0.1
adversary =
fileMidfix = f'{gradientWalker}-{iterations}'
parameterFileNameIn, parameterFileNameOut = None, ''.join(('mnistRBM-', fileMidfix, 'step.para'))
#parameterFileNameIn, parameterFileNameOut = f'mnistRBM-{gradientWalker}-1000000step.para', f'mnistRBM-{gradientWalker}-{iterations+1000000}step.para'
runTraining = True
verbose = False
mnistReconProbPlotFilePrefix = ''.join(('mnistReconProb-', fileMidfix, 'steps-'))
mnistReconPlotFilePrefix = ''.join(('mnistRecon-', fileMidfix, 'steps-'))
parameterHistogramFilePrefix = ''.join(('paraHistogram-', fileMidfix, 'steps-'))
gradientHistogramFilePrefix = ''.join(('gradHistogram-', fileMidfix, 'steps-'))
numReceptiveFields, receptiveFieldFilePrefix = 9, ''.join(('receptiveField-', fileMidfix, 'steps-'))
hiddenUnitActivationsSubset = rng.randint(numberHiddenUnits, size=numberHiddenUnits//10)
hiddenUnitActivationFilePrefix = ''.join(('hiddenUnitActivation-', fileMidfix, 'steps-'))
feFileName = ''.join(('fe-', fileMidfix, 'steps-'))
feRatioFileName = ''.join(('feRatio-', fileMidfix, 'steps-'))
if gradientWalker == 'sgd':
learningRate = 1e-4
elif gradientWalker == 'adam' or gradientWalker == 'adamAdversarial':
#learningRate = 1e-4
#learningRate = powerLawGenerator(1e-2, -0.1)
learningRate = powerLawGenerator(1e-3, -0.1)
adams = dict(zip(['visible', 'hidden', 'weights'],
[Adam(stepSize=learningRate) for _ in range(3)]))
else:
exit(1)
#setup RBM
visibleProportionOn = np.sum([images.sum(axis=0) for images in trainImagesByLabel], axis=0) / trainImages.shape[0]
#visibleProportionOn = None
visibleLayer = np.zeros(numberVisibleUnits)
hiddenLayer = np.zeros(numberHiddenUnits)
if parameterFileNameIn is not None:
with open(parameterFileNameIn, 'r') as parameterFile:
rbm = RestrictedBoltzmannMachine(visibleLayer, hiddenLayer,
temperature=temperature, sigma=sigma,
visibleProportionOn=visibleProportionOn,
parameterFile=parameterFile, rngSeed=internalRngSeed)
else:
rbm = RestrictedBoltzmannMachine(visibleLayer, hiddenLayer,
temperature=temperature, sigma=sigma,
visibleProportionOn=visibleProportionOn, rngSeed=internalRngSeed)
if gradientWalker == 'sgd':
updateParameters = lambda miniBatch, \
miniFantasyBatch: \
rbm.updateParametersSGD(miniBatch,
miniFantasyBatch,
learningRate,
nCDSteps=nCDSteps,
l1Coefficient=l1Coefficient,
l2Coefficient=l2Coefficient,
verbose=verbose)
elif gradientWalker == 'adam':
updateParameters = lambda miniBatch, \
miniFantasyBatch: \
rbm.updateParametersAdam(miniBatch,
miniFantasyBatch,
adams,
nCDSteps=nCDSteps,
l1Coefficient=l1Coefficient,
l2Coefficient=l2Coefficient,
verbose=verbose)
elif gradientWalker == 'adamAdversarial':
updateParameters = lambda miniBatch, \
miniFantasyBatch: \
rbm.updateParametersAdamAdversarial(miniBatch,
miniFantasyBatch,
adams,
gamma,
adversary,
nCDSteps=nCDSteps,
l1Coefficient=l1Coefficient,
l2Coefficient=l2Coefficient,
verbose=verbose)
else:
exit(1)
#build dict for parameter histogram output
weightParameterTypes = {'Visible': rbm.visibleBias,
'Hidden': rbm.hiddenBias,
'Weights': rbm.weights}
weightHistogramsByParameterType = {'Visible': [],
'Hidden': [],
'Weights': []}
gradientParameterTypes = {'Visible': rbm.visibleStep,
'Hidden': rbm.hiddenStep,
'Weights': rbm.weightStep}
gradientHistogramsByParameterType = {'Visible': [],
'Hidden': [],
'Weights': []}
historicalRBMs = []
hiddenUnitActivations = []
historicalFEs = []
trainSamplesForFE = getMiniBatchByLabel(trainImagesByLabel, miniBatchSize*10, rng)
testSamplesForFE = getMiniBatchByLabel(testImagesByLabel, miniBatchSize*10, rng)
setupEndTime = time.time()
if runTraining is True:
loopStartTime = time.time()
#build fantasy batch
miniFantasyBatch = np.copy(getMiniBatchByLabel(trainImagesByLabel, miniBatchSize, rng))
for i in range(iterations):
miniBatch = getMiniBatchByLabel(trainImagesByLabel, miniBatchSize, rng)
miniFantasyBatch = updateParameters(miniBatch, miniFantasyBatch)
if (i+1) % trainingReconstructionErrorOutputStride == 0:
print(i, rbm.computeReconstructionError(getMiniBatchByLabel(testImagesByLabel, miniBatchSize, rng)))
if (i+1) % trainingOutputStride == 0:
for parameterType in weightParameterTypes:
xs, ys, _ = diagnostics.computeHistogramArray(weightParameterTypes[parameterType].flatten())
weightHistogramsByParameterType[parameterType].append((i, xs, ys))
xs, ys, _ = diagnostics.computeHistogramArray(gradientParameterTypes[parameterType].flatten())
gradientHistogramsByParameterType[parameterType].append((i, xs, ys))
historicalRBMs.append((i, rbm.copy()))
hiddenUnitActivations.append((i, rbm.storeHiddenActivationsOnMiniBatch(miniBatch,
hiddenUnits=hiddenUnitActivationsSubset)))
historicalFEs.append((i,
rbm.computeMeanFreeEnergy(trainSamplesForFE),
rbm.computeMeanFreeEnergy(testSamplesForFE)))
loopEndTime = time.time()
if parameterFileNameOut is not None:
with open(parameterFileNameOut, 'w') as parameterFile:
rbm.dumpParameterFile(parameterFile)
outputStartTime = time.time()
#plot reconstruction series
visibleStarts = getMiniBatchByLabel(testImagesByLabel, 10, rng)
plotReconstructionSeries(visibleStarts, rbm, mnistReconProbPlotFilePrefix, mnistReconPlotFilePrefix)
#plot fantasy particle series
visibleStarts = miniFantasyBatch
for i, visible in enumerate(visibleStarts):
rbm.visibleLayer = visible
for _ in range(equilibrateFantasyForOutput):
visibleStarts[i], _ = rbm.gibbsSample(hiddenUnitsStochastic=False)
plotReconstructionSeries(visibleStarts, rbm, mnistReconProbPlotFilePrefix+'fantasy', mnistReconPlotFilePrefix+'fantasy')
#plot parameter histograms
plotParameterHistograms(weightHistogramsByParameterType, gradientHistogramsByParameterType, parameterHistogramFilePrefix, gradientHistogramFilePrefix)
#plot receptive fields
hiddenUnitIndices = rng.randint(rbm.hiddenBias.shape[0], size=numReceptiveFields)
for i, historicalRBM in historicalRBMs:
receptiveFieldFileName = ''.join((receptiveFieldFilePrefix,
f'{i}.pdf'))
plotReceptiveFields(historicalRBM, hiddenUnitIndices, fileName=receptiveFieldFileName)
#plot hidden unit activations
for i, hiddenUnitActivation in hiddenUnitActivations:
hiddenUnitActivationFileName = ''.join((hiddenUnitActivationFilePrefix,
f'{i}.pdf'))
diagnostics.plotHiddenActivationsOnMiniBatch(hiddenUnitActivation,
fileName=hiddenUnitActivationFileName)
#plot FE vs time
t = [fe[0] for fe in historicalFEs]
trainFE = np.array([fe[1] for fe in historicalFEs])
testFE = np.array([fe[2] for fe in historicalFEs])
diagnostics.plotTrainingTestAverageFEVsTime(t, trainFE, testFE, fileName=feFileName)
diagnostics.plotTrainingTestAverageFEVsTime(t, trainFE/testFE, None, fileName=feRatioFileName)
outputEndTime = time.time()
print(f'setup time {setupEndTime-setupStartTime}s')
if runTraining is True:
print(f'training loop time {loopEndTime-loopStartTime}s')
print(f'output time {outputEndTime-outputStartTime}s')
print('Build model...')
model = Sequential()
model.add(Embedding(max_features, 32))
model.add(LSTM(32, dropout=0.2, recurrent_dropout=0.2))
model.add(Dense(1, activation='sigmoid'))
plot_model(model, to_file='model_imdb.png', show_shapes=True)
results_acc = []
result_acc = []
results_loss = []
result_loss = []
test_acc_results = []
test_loss_results = []
l = [
Adam(lr=0.001, amsgrad=True),
AAdam(lr=0.001, amsgrad=True),
Adam(lr=0.001, amsgrad=False),
AAdam(lr=0.001, amsgrad=False),
Adagrad(),
AAdagrad(),
SGD(),
ASGD()
] #, Adam(lr=0.001, amsgrad = True), AAdam(lr=0.001, amsgrad = True)]
for opt in l:
model.compile(loss='binary_crossentropy',
optimizer=opt,
metrics=['accuracy'])
#model.save_weights('initial_weights_imdb.h5')
def setup_train(self):
# dimensions: (batch, time, 12)
chord_types = T.btensor3()
# dimensions: (batch, time)
chord_roots = T.imatrix()
# dimensions: (batch, time)
relative_posns = [T.imatrix() for _ in self.encodings]
# dimesions: (batch, time, output_data)
encoded_melodies = [T.btensor3() for _ in self.encodings]
# dimesions: (batch, time)
correct_notes = T.imatrix()
n_batch, n_time = chord_roots.shape
def _build(det_dropout):
all_out_probs = []
for encoding, lstmstack, encoded_melody, relative_pos in zip(self.encodings, self.lstmstacks, encoded_melodies, relative_posns):
activations = lstmstack.do_preprocess_scan( timestep=T.tile(T.arange(n_time), (n_batch,1)) ,
relative_position=relative_pos,
cur_chord_type=chord_types,
cur_chord_root=chord_roots,
last_output=T.concatenate([T.tile(encoding.initial_encoded_form(), (n_batch,1,1)),
encoded_melody[:,:-1,:] ], 1),
deterministic_dropout=det_dropout)
out_probs = encoding.decode_to_probs(activations, relative_pos, self.bounds.lowbound, self.bounds.highbound)
all_out_probs.append(out_probs)
reduced_out_probs = functools.reduce((lambda x,y: x*y), all_out_probs)
if self.normalize_artic_only:
non_artic_probs = reduced_out_probs[:,:,:2]
artic_probs = reduced_out_probs[:,:,2:]
non_artic_sum = T.sum(non_artic_probs, 2, keepdims=True)
artic_sum = T.sum(artic_probs, 2, keepdims=True)
norm_artic_probs = artic_probs*(1-non_artic_sum)/artic_sum
norm_out_probs = T.concatenate([non_artic_probs, norm_artic_probs], 2)
else:
normsum = T.sum(reduced_out_probs, 2, keepdims=True)
normsum = T.maximum(normsum, constants.EPSILON)
norm_out_probs = reduced_out_probs/normsum
return Encoding.compute_loss(norm_out_probs, correct_notes, True)
train_loss, train_info = _build(False)
updates = Adam(train_loss, self.get_optimize_params(), lr=self.learning_rate_var)
eval_loss, eval_info = _build(True)
self.loss_info_keys = list(train_info.keys())
self.update_fun = theano.function(
inputs=[chord_types, chord_roots, correct_notes] + relative_posns + encoded_melodies,
outputs=[train_loss]+list(train_info.values()),
updates=updates,
allow_input_downcast=True,
on_unused_input='ignore',
mode=(NanGuardMode(nan_is_error=True, inf_is_error=True, big_is_error=True) if self.nanguard else None))
self.eval_fun = theano.function(
inputs=[chord_types, chord_roots, correct_notes] + relative_posns + encoded_melodies,
outputs=[eval_loss]+list(eval_info.values()),
allow_input_downcast=True,
on_unused_input='ignore',
mode=(NanGuardMode(nan_is_error=True, inf_is_error=True, big_is_error=True) if self.nanguard else None))
# numpy.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
importlib.reload(model)
net = getattr(model,
model_name.upper())(input_channel=input_channel,
input_size=input_size,
output_size=output_size).to(device)
if use_cuda:
net = DataParallelWithCallback(net)
print(f"===> Model:\n{list(net.modules())[0]}")
print_param(net)
if model_name == 'cnn0' or model_name == 'cnn1':
optimizer = Adam(net.parameters(),
lr=grid['lr'],
l1=grid['l1'],
weight_decay=grid['l2'],
amsgrad=True)
elif model_name == 'vgg19' or model_name == 'cnn2' or model_name == 'cnn3':
optimizer = Adam([{
'params':
iter(param for name, param in net.named_parameters()
if 'channel_mask' in name),
'l1':
grid['l1_channel']
}, {
'params':
iter(param for name, param in net.named_parameters()
if 'spatial_mask' in name),
'l1':
grid['l1_spatial']
loss_func = L.NegativeSampling(args.unit, cs, 20)
elif args.out_type == "original":
loss_func = SoftmaxCrossEntropyLoss(args.unit, n_vocab)
else:
raise Exception("Unknown output type: {}".format(args.out_type))
if args.model == "skipgram":
model = SkipGram(n_vocab, args.unit, loss_func)
elif args.model == "cbow":
model = ContinuousBow(n_vocab, args.unit, loss_func)
else:
raise Exception('Unknown model type:'.format(args.model))
dataset = np.array(dataset, dtype=np.int32)
optimizer = Adam()
optimizer.setup(model)
begin_time = time.time()
cur_at = begin_time
word_count = 0
skip = (len(dataset) - args.window * 2) // args.batchsize
next_count = 100000
for epoch in range(args.epoch):
accum_loss = 0
print('epoch: {0}'.format(epoch))
indexes = np.random.permutation(skip)
for i in indexes:
if word_count >= next_count:
now = time.time()
duration = now - cur_at
